Many large software systems are variability-rich, object-oriented, and implemented in a single code base. They then rely on multiple traditional techniques (inheritance, patterns) to realize variability, making these implementations not explicit. This directly hampers the comprehension of variability implementations, especially for newcomers in a project that need, in a short time, to understand the most important parts. In this paper, we propose VariCity, a visualization using the city metaphor to exhibit zones of interest, being zones of high density of variability implementations. The different forms of variability implementations are first detected through the usage of symmetries in code (e.g., inheritance defines a substitution symmetry between the immutable part of the superclass and the possible changes in its subclasses). VariCity then creates a 3D city representation with buildings being classes while the metrics on the number of symmetries (e.g., the number of overloaded methods, influence the building size, and their color if they are heavily loaded in symmetries). Contrary to the usual package-based organization in code-related city representations, the city streets are arranged according to the usage relationships between classes. Inheritance is simply represented with hoverable aerial links. Variability-related design patterns are depicted as buildings with specific geometric forms, while some classes specified as entry points can help in shaping the whole city organization. We also report on the evaluation of VariCity on a set of large object-oriented systems, showing that several usage scenarios helping a newcomer to spot critical variability-related zones are covered.

Embedded systems, like those found in the automotive domain, must comply with stringent functional and non-functional requirements. To fulfil these requirements, engineers are confronted with a plethora of design alternatives both at the software and hardware level, out of which they must select the optimal solution wrt. possibly-antagonistic quality attributes (e.g. cost of manufacturing vs. speed of execution). We propose a model-driven framework to assist engineers in this choice. It captures high-level specifications of the system in the form of variable dataflows and configurable hardware platforms. A mapping algorithm then derives the design space, i.e. the set of compatible pairs of application and platform variants, and a variability-aware executable model, which encodes the functional and non-functional behaviour of all viable system variants. Novel verification algorithms then pinpoint the optimal system variants efficiently. The benefits of our approach are evaluated through a real-world case study from the automotive industry.

Smart buildings and smart cities rely on interconnected sensor networks that collect data about their environment to support various applications. Developing and deploying the data collection architectures of these systems is a challenging problem. The specificities of the sensor platforms compel software engineers to work at a low level. This makes this activity tedious, producing code that badly exploits the network architecture, and hampering reuse of data collection policies. Moreover, several data collection programs cannot be guaranteed to be deployable on a shared infrastructure. In this paper, we present an automated approach that supports (i) the definition of data collection policies at a higher level of abstraction, (ii) the representation of the diverse platforms and the network topology, and (iii) the automatic composition and deployment of the policies on top of heterogeneous sensing infrastructures following different strategies. The approach is tooled and has been assessed on both realistic and simulated deployments.

Concern-Oriented Reuse (CORE) proposes a new way of structuring model-driven software development, where models of the system are modularized by domains of abstraction within units of reuse called concerns. Within a CORE concern, models are further decomposed and modularized by features. This paper extends CORE with a technique that enables developers of high-level concerns to reuse lower-level concerns without unnecessarily committing to a specific feature selection. The developer can select the functionality that is minimally needed to continue development, and reexpose relevant alternative lower-level features of the reused concern in the reusing concern's interface. This effectively delays decision making about alternative functionality until the higher-level reuse context, where more detailed requirements are known and further decisions can be made. The paper describes the algorithms for composing the variation (i.e., feature and impact models), customization, and usage interfaces of a concern, as well as the concern's realization models and finally an entire concern hierarchy, as is necessary to support delayed decision making in CORE.

In Software Product Line (SPL) engineering, Feature Models (FMs) are widely used to capture and manage variability in a sound and organized fashion. Though semantics, notations and reasoning support are well established, maintaining large FMs is still an open problem. As large FMs naturally contain different concerns, some related to domains, others being inherently cross-cutting ones, it is challenging to find a decomposition that will tame this complexity and ease maintenance. This paper presents a visual representation of dependent FMs useful in decomposing a large FM while quantitatively visualizing constraints between and inside them. This Variability Blueprint is intuitive enough to enable the SPL maintainer to confine dependencies between FMs in a small set of identified features inside each decomposed FM. We describe our blueprint and report on its application on two case studies.

As Software Product Lines (SPLs) are now more widely applied in new application fields such as IT or Web systems, complex and large-scale configurations have to be handled. In these fields, the strong domain orientation leads to the need to manage interrelated SPLs and multiple instances of configured sub-products, resulting in complex configurations that cannot be easily represented by simple sets of features. In this paper we propose a tooled approach to manage such SPLs through a domain model that interrelates several feature models in a consistent way. The approach thus shifts part of the domain knowledge to the problem space and supports the derivation of complex configurations with multiple instantiations and associations of sub-products. We also report on the application of our approach to an industrial-strength software development in the field of digital signage.

In software product line (SPL) engineering, a software configuration can be obtained through a valid selection of features represented in a feature model (FM). With a strong separation between requirements and reusable components and a deep impact of high level choices on technical parts, determining and configuring an well-adapted software configuration is a long, cumbersome and error-prone activity. This paper presents a modeling process in which variability sources are separated in different FMs and inter-related by propositional constraints while consistency checking and propagation of variability choices are automated. We show how the variability requirements can be expressed and then refined at design time so that the set of valid software configurations to be considered at run time may be highly reduced. Software tools support the approach and some experimentations on a video surveillance SPL are also reported.

Feature models (FMs) are a popular formalism for describing the commonality and variability of software product lines (SPLs) in terms of features. As SPL development increasingly involves numerous large FMs, scalable modular techniques are required to manage their complexity. In this paper, we present a novel slicing technique that produces a projection of an FM, including constraints. The slicing allows SPL practitioners to find semantically meaningful decompositions of FMs and has been integrated into the FAMILIAR language.

Variability in software product lines is often expressed through feature models (FMs). To handle the complexity of increasingly larger FMs, we propose semantically meaningful decomposition support through a slicing operator. We describe how the slicing operator is integrated into the FAMILIAR environment and how it can be combined with other operators to support complex tasks over FMs in different case studies.